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Enhancing CNN for Forensics Age Estimation Using CGAN and pseudo-labelling

Computers, Materials & Continua 2023年第2期74卷 2499-2516页

作者： Sultan Alkaabi Salman Yussof Sameera Al-Mulla Institute of Informatics and Computing in Energy Universiti Tenaga NasionalMalaysia College of Engineering and IT University of DubaiUAE

Age estimation using forensics odontology is an important process in identifying victims in criminal or mass disaster ***,this process is done manually by human ***,the speed and accuracy may vary depending on the expertise level of the human expert and other human factors such as level of fatigue and *** improve the recognition speed and consistency,researchers have proposed automated age estimation using deep learning techniques such as Convolutional Neural Network(CNN).CNN requires many training images to obtain high percentage of recognition ***,it is very difficult to get large number of samples of dental images for training the CNN due to the need to comply to privacy acts.A promising solution to this problem is a technique called Generative Adversarial Network(GAN).GAN is a technique that can generate synthetic images that has similar statistics as the training set.A variation of GAN called Conditional GAN(CGAN)enables the generation of the synthetic images to be controlled more precisely such that only the specified type of images will be *** paper proposes a CGAN for generating new dental images to increase the number of images available for training a CNN model to perform age *** also propose a pseudolabelling technique to label the generated images with proper age and *** used the combination of real and generated images to trainDentalAge and Sex Net(DASNET),which is a CNN model for dental age *** on the experiment conducted,the accuracy,coefficient of determination(R2)and Absolute Error(AE)of DASNET have improved to 87%,0.85 and 1.18 years respectively as opposed to 74%,0.72 and 3.45 years when DASNET is trained using real,but smaller number of images.

关键词： Dental forensics age estimation generative adversarial network pseudo-labelling convolutional neural network

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